Search Results (32,711)

This paper investigates ship–pier collision risk caused by yaw deviation in inland bridge waterways. The proposed framework is conceived for fixed auxiliary thruster installation in bridge areas, rather than retrofitting shipboard propulsion systems. A proactive intervention scheme is developed based on state estimation and short-horizon prediction. A Kalman filter is used for state fusion and short-horizon motion prediction. Yaw events are detected via a threshold rule with consecutive-decision logic. An extended state observer (ESO) is adopted to estimate lumped disturbances and model uncertainties. A fuzzy self-tuning PID law is then applied to generate thruster commands for closed-loop corrective control. Numerical simulations suggest that, relative to rudder-only recovery, thruster-assisted intervention yields improved restoration behavior, reduced lateral deviation accumulation, and increased minimum clearance to bridge piers under the tested conditions. Additional tests with cross-current disturbances indicate that the risk-triggered scheme with ESO-based compensation can maintain stable recovery and a higher safety margin. The proposed approach provides an engineering-oriented pathway to extend bridge-area risk management from warning-level assessment to executable control intervention. Full article

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence capability. First, the influence mechanism of fluid materials on construction robots and their trajectory tracking control features are explored, and the design approach for the extended reference trajectory is elaborated. Subsequently, a nonsingular sliding surface with predefined-time convergence is constructed, and a RBF neural network with convergent weight vectors is established to approximate the composite disturbances existing in the robot system. On the basis of the proposed predefined-time convergent super-twisting control theory, a super-twisting sliding mode controller tailored for construction robots is devised, and the predefined-time convergence performance of the closed-loop system is theoretically validated. Numerical simulation results indicate that the proposed algorithm can guarantee that the construction robot’s angles move accurately along the actual reference trajectory, with the angular tracking error achieving a precision of $3\times {10}^{-6}$ rad, thereby confirming the feasibility and effectiveness of the proposed method. Full article

Sugarcane (Saccharum spp.) is a globally vital sugar crop, yet its productivity faces severe challenges from infestation by Chilo sacchariphagus. To decipher the plant’s molecular and metabolic defense mechanisms, this study applied an integrated transcriptomic and metabolomic analysis to three field-grown sugarcane cultivars (Zhongtang 4, 5, and 6) under natural borer stress. The transcriptomic analysis identified a total of 34,004 differentially expressed genes (DEGs), of which 18,674 were up-regulated, and 15,330 were down-regulated. The three cultivars exhibited distinct transcriptional regulatory patterns: Z4 and Z5 showed a global suppression-type response and a strong activation-type response, respectively, and Z6 presented a balanced-type response. A functional enrichment analysis revealed that the DEGs were significantly involved in metabolic processes, stress response, plant hormone signal transduction, phenylpropanoid biosynthesis, and plant-pathogen interaction pathways. Metabolomic analysis detected 963 differentially accumulated metabolites (DAMs), primarily including flavonoids, phenolic acids, amino acids and their derivatives, and lipids. These metabolites were significantly enriched in pathways such as amino acid metabolism, biosynthesis of secondary metabolites, and glutathione metabolism. Integrated multi-omics analysis further revealed strong synergistic regulatory relationships between gene expression and metabolite accumulation, particularly in defense-related secondary metabolic pathways, such as phenylpropanoid and flavonoid biosynthesis. Several key regulatory hubs were identified, including novel transcripts and D-xylulose-5-phosphate. Sugarcane employs a genetic background-dependent, multi-layered transcriptional reprogramming and metabolic restructuring to cope with borer stress. Cultivars Z4 and Z6 tend to activate and accumulate defensive compounds, while Z5 exhibits a different pattern of metabolic resource allocation. This research provides a systematic elucidation of the molecular mechanisms underlying insect resistance in sugarcane and offers important candidate genes and metabolites for breeding resistant varieties. Full article

Objectives: The TNM staging system for distal cholangiocarcinoma (dCCA) has limited accuracy due to its anatomical basis. This study developed a prognostic model integrating inflammatory-nutritional markers and tumor biomarkers to improve risk stratification. Methods: We analyzed 208 dCCA patients undergoing pancreaticoduodenectomy (2017–2024). Independent prognostic factors for overall survival (OS) were identified via Cox regression, including tumor marker (corrected CA19-9) and host status markers (PLR, CAR, and PNI). A nomogram was constructed and evaluated using calibration, ROC, and DCA. Patients were risk-stratified using the model’s score. Results: Four independent factors were identified: corrected CA19-9 (HR = 2.438), PLR (HR = 2.041), CAR (HR = 2.477), and PNI (HR = 0.415). The nomogram showed excellent discrimination for 1-, 3-, and 5-year OS (AUC: 0.847, 0.824, 0.858), good calibration, and clinical utility per DCA. Risk stratification significantly distinguished high-risk (n = 110) from low-risk (n = 98) groups (log-rank p < 0.0001). Discussion: This multidimensional model (tumor burden, inflammation, nutrition) outperforms TNM staging, highlighting host systemic status. Despite its single-center retrospective design, it shows promise for personalized risk assessment. Conclusion: The CINS (Cholangiocarcinoma Inflammation–Nutrition Score) accurately predicts prognosis and effectively risk-stratifies dCCA patients, aiding personalized treatment planning. Full article

Peer relationships are closely associated with the academic performance of adolescent students. This paper develops an integrated framework taking the peer main effect as the starting point to systematically incorporate the demonstration effect, the within-class network effect, and the cross-class average effect. Using comprehensive network and survey data from high school students in a typical county in western China, this paper employs a network-based identification strategy to reveal robust positive peer effects. The mechanism test shows that the demonstration effect can play a moderating role in peer effects on academic performance, with the network effect being heterogeneous across students and the average effect indicative of a potential role in providing diverse academic information. These findings provide empirical insights into the multifaceted nature of peer dynamics, offering actionable evidence for designing targeted interventions to improve educational outcomes in underdeveloped regions. Full article

Background: Tubular aggregate myopathy (TAM) is an autosomal dominant myopathy that results from gain-of-function mutations in the STIM1 and ORAI1 genes, which encode the two key proteins that coordinate store-operated Ca²⁺ entry in skeletal muscle and other cell types. Knock-in mice heterozygous for a glycine-to-serine point mutation in the ORAI1 pore (ORAI1^G100S/+ or GS mice) phenocopy several key aspects of TAM in humans with the analogous mutation including muscle weakness, exercise intolerance, elevated CK levels, hypocalcemia, and the presence of tubular aggregates. Methods: Since homozygous inheritance of the ORAI1-G100S mutation is embryonic lethal, we assessed the impact of homotypic ORAI1-G100S expression in skeletal muscle by crossing GS mice with constitutive, muscle-specific ORAI1 knock-in mice (cORAI1-KO). Results: Compound cORAI1-KO/GS mice exhibit only one active ORAI1 (GS) allele, and thus only express ORAI1-G100S monomers in skeletal muscle (‘homotypic’ GS mice). Homotypic GS mice exhibit an earlier onset and more severe muscle phenotype than age-matched heterotypic GS mice with both WT and GS alleles. Specifically, homotypic GS mice exhibit TAs at an earlier age, as well as significantly reduced in vivo muscle performance (grip strength, treadmill endurance, and rotarod endurance), maximal specific force production, and respiratory function, compared to those observed for both WT and heterotypic GS mice. Conclusions: These findings indicate that homotypic expression of the ORAI1-G100S mutation in skeletal muscle results in an earlier-onset and more severe muscle phenotype. Full article

Schistosomiasis elimination is increasingly constrained less by the technical efficacy of single interventions than by systemic dynamics in coupled human–animal–environment settings, including nonlinear feedback, spatial heterogeneity, and cross-sectoral govern frictions. We conducted a systematic methodological review (search date: 1 January 2026) across PubMed, Web of Science, Scopus, EconLit, and CNKI to identify studies that (i) addressed schistosomiasis control, (ii) used explicit system-based, causal, or network-oriented analytical structures, and (iii) incorporated economic evaluation with multi-domain outcomes. We synthesized modeling architectures, economic methods, and approaches to trade-offs and uncertainty, and applied an evidence-informed systemic causality framework to assess decision-analytic adequacy. The literature grouped into three related strands: transmission and system dynamics models that capture feedback processes and rebound risks; economic evaluations dominated by cost-effectiveness analyses; and cross-sectoral or surveillance-oriented decision models optimizing implementation under resource constraints. Across strands, elimination-stage investments such as surveillance, environmental management, and coordination exhibit strong externalities and quasi-public-good properties that are systematically undervalued in single-sector, single-metric frameworks. We argue that decision-relevant evaluation should be reframed as a multi-objective resource allocation problem that integrates systemic modeling with economic valuation, explicitly addresses uncertainty, and applies multi-criteria decision analysis to support long-horizon, cross-sectoral decision-making. Full article

This study provides a systematic and multidimensional evaluation of flesh quality differences between triploid (T-PZ) and allotetraploid (A-PZ) Pengze crucian carp, addressing a significant gap in genetic improvement of this economically important aquaculture species. By integrating proximate composition analysis, detailed amino acid and fatty acid profiling, volatile flavor compounds, texture characteristics, and non-targeted muscle metabolomics, we delineated the distinct quality attributes associated with each cytotype. The results showed that T-PZ possessed significantly higher crude protein and histidine content, along with a superior flavor profile characterized by lower relative levels of fishy odor-associated aldehydes such as hexanal, heptanal, and nonanal. In contrast, A-PZ exhibited significantly elevated crude lipid, total fatty acid, SFA, MUFA, PUFA, and EPA + DHA contents; a higher essential amino acid index; and improved tenderness indicated by significantly lower hardness and chewiness. Metabolomic analysis identified 216 significantly different metabolites, notably enriched in key pathways including glycine, serine, and threonine metabolism; arginine biosynthesis; and glycerophospholipid metabolism. These comprehensive findings elucidate the complementary nutritional and sensory strengths of the two ploidy forms, thereby establishing a crucial scientific foundation for targeted, quality-driven breeding programs aimed at optimizing flesh quality in Pengze crucian carp for the aquaculture industry. Full article

Managing trans-jurisdictional water scarcity conflicts is a thorny task, as it is usually challenged by multiple institutionally independent decision-making agents, which requires developing cooperative and self-enforcing solutions. This study proposed a decision-making method that combines fairness and stability concerns to solve trans-jurisdictional water allocation conflicts under scarcity. Based on the water allocation alternatives yielded by seven bankruptcy rules, the Gini coefficient and Shapley–Shubik power index were used to separately quantify their fairness and stability criteria from distributive justice and individual-level acceptability, and then game theory was employed to integrate the quantitative results of the two criteria to make final water allocation decisions. The decision-making method was applied to the Hanjiang River Basin of Hubei Province in China under two water scarcity scenarios, which is shared by ten city-level jurisdictions. Numerical results indicate that bankruptcy rules are considered practical for performing trans-jurisdictional water allocations under scarcity, but their realistic eligibility should be investigated before implementation. An apparent trade-off between fairness and stability exists among the water allocation alternatives, and there exists room for identifying compromise alternatives; the constrained equal losses and adjusted proportional rules are identified as the preferred rule by the proposed decision-making method for allocating water resources in the Hanjiang River Basin of Hubei Province, respectively, in dry and extremely dry years. The findings highlight the necessity and significance of balancing fairness and stability criteria in managing trans-jurisdictional water scarcity conflicts, and the proposed method has proved to be an effective decision-making tool to facilitate negotiation over trans-jurisdictional water allocations under scarcity. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

Background: Brugada syndrome (BrS) is a genetic cardiac arrhythmia disorder inherited in an autosomal dominant manner, characterized by ST-segment elevation in the right precordial leads (V1–V3) on electrocardiograms (ECGs). This syndrome predominantly affects young individuals with structurally normal hearts and significantly increases the risk of ventricular arrhythmias and sudden cardiac death (SCD). The most common genotype found among BrS patients is caused by variants in the SCN5A gene, which lead to a loss of function of the cardiac sodium channel Nav1.5 by different mechanisms. Methods: Plasmids containing SCN5A were constructed using PCR and site-directed mutagenesis to create the D372H variant. HEK293 cells were cultured and transfected with the WT, D372H, or a combination of both plasmids. Patch-clamp recordings assessed sodium current characteristics. Confocal microscopy visualized channel localization. Quantitative RT-PCR was used to analyze mRNA expression levels, while Western blot evaluated protein expression using specific antibodies. Results: In HEK293 cells expressing the D372H mutant, functional assays revealed a near-complete loss of sodium currents. Co-transfection of WT and D372H plasmids resulted in a significant reduction in current density compared with WT alone, while activation, inactivation, and recovery kinetics were unaffected. In addition, both the mutant protein and protein expressed in co-transfected cells exhibited reduced fluorescence intensity, indicating decreased expression levels. These findings were further supported by Western blot and RT-qPCR analyses. Conclusions: In summary, our findings indicate that the D372H variant produces a marked reduction in Nav1.5 function through reduced sodium current density and decreased channel expression. Given its critical position within the DI-pore loop, this defect is expected to markedly diminish the inward sodium current necessary for normal depolarization. Such impaired excitability—particularly relevant in the right ventricular outflow tract—may accentuate regional differences in repolarization and create conditions that favor reentrant activity. These findings provide mechanistic insights into how the p.D372H variant alters Nav1.5 channel function in vitro and offer functional evidence that may assist in interpreting its potential relevance to Brugada syndrome. Full article

As smart grids evolve into complex cyber-physical systems, conventional static defenses struggle to address time-varying topologies and Advanced Persistent Threats (APTs). We propose the Security Framework for Resilient Smart Grids based on Self-Organizing Graph Neural Cellular Automata (SG-GNC). Specifically, a Neural Homeostatic Embedding (NHE) mechanism utilizes variational graph autoencoders to construct a continuous health manifold for unsupervised anomaly detection, while a Neural Cellular Automata (NCA) engine employs shared-weight local rules to empower nodes with decentralized self-healing capabilities. Finally, a Generative Adversarial Immunity (GAI) strategy facilitates active defense co-evolution, enhancing robustness against zero-day attacks. Experimental results on the IEEE 118 and 300-bus systems demonstrate an average detection accuracy of 98.23%, significantly outperforming benchmarks. In scenarios involving dynamic topology and zero-day attacks, the framework maintains over 96% accuracy with an inference latency of only 9.45 ms. These findings validate the capability of SG-GNC to provide resilient, endogenous defense in complex heterogeneous environments. Full article

The scale mismatch between wind turbine hub heights and conventional meteorological masts introduces uncertainties in wind resource assessment. Vertical wind speed extrapolation serves as a critical technique to bridge this spatial gap. Current extrapolation paradigms struggle with two fundamental limitations. Physical models fail to capture non-stationary atmospheric stability, whereas purely data-driven methods depend heavily on unavailable hub-height ground truth. To bridge this gap, this paper proposes a Physically Guided Neural Network framework. By integrating physical boundary-layer principles with an adaptive residual correction mechanism, the model introduces an inductive bias that maps near-surface observations to dynamic wind shear evolutions. The network employs a “Near-Surface Learning and Hub-Height” Transfer strategy. This approach optimizes the model exclusively on multi-level observations from 10 to 70 m to eliminate the dependency on high-altitude target labels. Validation on a 100 MW wind farm dataset, utilizing a 70 m proxy variable evaluation, demonstrates that this framework reduces the wind speed extrapolation root mean square error by 56.48% compared to traditional power law models. Furthermore, downstream theoretical power estimation errors are reduced by 10.72%, effectively mitigating power curve lag phenomena. This hybrid approach establishes a robust and low-cost paradigm for refined wind energy assessment in engineering scenarios lacking tall meteorological monitoring. Full article

In recent years, the gut microbiome has been increasingly recognized as an important factor in regulating treatment responses and disease progression in prostate cancer (PCa). We synthesized literature published over the past five years, focusing on preclinical and clinical studies linking the microbiome to PCa treatment outcomes. There is accumulating evidence that gut microbiota dysbiosis and its associated metabolites can modulate key biological processes, such as androgen metabolism, inflammatory signaling pathways, and antitumor immune responses. These processes affect the sensitivity of PCa patients to androgen deprivation therapy (ADT) and other systemic treatments. The available evidence suggests that the gut microbiome has the potential to serve as a predictive biomarker for treatment response and could represent a novel target for interventional precision therapy in PCa. This narrative review summarizes the latest research on the “gut–prostate axis”, focusing on the role of the gut microbiome in regulating therapeutic responses in PCa and the underlying mechanisms. Finally, we address current limitations, including the predominance of preclinical evidence, methodological heterogeneity, and the critical need for longitudinal clinical validation to distinguish causality from association. Full article

As Guangdong is a pivotal province in China’s national forest city initiative, examining the spatiotemporal evolution and key drivers of eco-environmental quality (EEQ) in Guangdong is essential for advancing regional sustainable development. To address the complexity of EEQ assessments in areas that are characterized by dense hydrological networks, extensive vegetation cover, and rapid urban expansion, the Google Earth Engine platform was utilized in this study, and remote sensing indices with heightened sensitivity to vegetation and moisture dynamics—namely, the kernel normalized difference vegetation index and the kernel normalized difference moisture index—were introduced to develop an improved water benefit-based ecological index (ImWBEI). Through an integrated analytical framework incorporating Theil–Sen trend analysis, Mann–Kendall significance testing, Hurst exponent analysis, an optimal parameter-based geographical detector, and a coupled coordination degree model, this research systematically evaluated the spatiotemporal patterns, future trends, driving mechanisms, and coordination with urbanization of the EEQ in Guangdong from 2000 to 2021. The results demonstrated that the ImWBEI enhanced the detailed characterization of complex underlying surfaces, such as urban built-up areas and land–water transition zones. Throughout the study period, the EEQ in Guangdong displayed a stable spatial distribution characterized by higher values in the north and lower values in the south. Concurrently, the EEQ significantly improved at a rate of 0.0092 per year. Hurst index analysis indicated that this trajectory would likely persist, with the future trend dominated by a pattern of weak persistent improvement. The comprehensive urbanization index was identified as the most critical factor influencing the spatial differentiation of the EEQ in Guangdong. Although notable north–south disparities were observed in the coordination between the EEQ and comprehensive urbanization, the provincial-level coupled coordination consistently improved. Consequently, this work yielded actionable insights and a replicable framework for ecological monitoring and coordinated development in similar water–forest integrated urban regions. It was particularly relevant for informing ecological restoration prioritization and development restriction decisions in critical land–water transition zones—areas where the ImWBEI demonstrated enhanced sensitivity. Full article